International Journal of Innovations in Science & Technology
Not a member yet
813 research outputs found
Sort by
Exploring the Influence of Teacher\u27s Stress and Emotions on Student Behavior in Secondary Schools
Full text linkThe focus of this study is on the intricate relationship between the stress of the teacher, the emotion of the teacher, and the behavior of the workers in secondary school classrooms, stressing the bidirectional relation (between) the emotional well-being of (the teacher) and the engagement of (the worker). Qualitative data from interviews with teachers and students are drawn on to investigate how emotional exhaustion, workload pressures, and the absence of institutional support prevent teacher regulation of emotion, and inhibit effective classroom management. What they found was that students can pick up on teacher stress, often based on what students perceive to be nonverbal cues, tone, and expressions, and those interpretations lead to the classroom behaviors of the students. Positive emotions by teachers develop trust, focus, and engagement, whereas frustration, inconsistency, and unpredictability engender student disengagement, increased disruptive behaviors, and strained relationships between teachers and students. These same emotional dynamics are catalyzed further by cultural factors; for instance, hierarchical power structures influence the context within which students respond to teacher stress. The systemic institutional support mechanisms are also lacking resulting in a cascade of emotional exhaustion followed by diminished classroom effectiveness. This study highlights the importance of emotional intelligence training, proactive classroom management strategies, and robust institutional support systems in a bid to reduce teacher stress and create a stable, positive learning environment. These results add to existing literature on the emotional well-being of teachers and provide practical suggestions to educational policymakers, administrators, and teacher training programs on addressing the emotional and psychological demands of teachers in today’s classrooms
Barriers to AI Adoption in Education: Insights from Teacher\u27s Perspectives
Full text linkA
rtificial intelligence in education is capable of offering significant benefits in the form of content generation, personalized learning, assistance in administration, and analytical reports. Despite the benefits, the integration of AI in education faces several challenges hindering its integration. The barriers to AI adoption in education are critical to explore, as they affect the incorporation of innovative educational technologies. The study aimed to explore the perceived barriers to suggest practical recommendations to enable educators to embrace innovative AI technologies for teaching. This study employed a qualitative research design with a descriptive research approach. A purposive sampling method was applied to select public and private sector educators from schools, colleges, and universities in Pakistan. Data were collected using an open-ended questionnaire designed using Google Forms. Data were analyzed using thematic analysis to recognize and categorize patterns and themes in responses, gaining a thorough understanding of the key barriers to AI adoption. The insights revealed that integrating AI in education inherits barriers in user experience, technological, and skills limitations, content reliability, privacy and security concerns, and overdependence on AI a risk to reduce creativity and learning. To overcome the barriers, clear ethical guidelines and policies, a balanced integration of AI with pedagogy, AI literacy training, and support to enable teachers to effectively use AI in education are recommended
Geospatial Nexus of Land Use Land Cover dynamics and Rapid Population Growth with Emphasis on Trend Prediction of Built-Up Areas in District Hangu, Pakistan
Full text linkThis study aims to analyze the land use land cover (LULC) and predict the patterns in the built-up areas of District Hangu and examine how population growth affects land use and land cover. Rapid population increase remains a continuous threat to the district’s land resources. Projections show that the population will grow from 518,811 in 2017 to 833,964 by 2051. Along with this growth, there is an ongoing expansion of underground utilities and infrastructure, driven by demographic pressures and urban development. The Logistic Regression (LR) model was used to forecast an expansion in the district’s built-up area. Through this model, potential zones for future development are identified, and anticipated changes in planned Land Use/Land Cover (LU/LC) are evaluated. All variables were transformed into raster format and standardized to a 0–1 range using a raster calculator, ensuring uniformity in statistical comparisons. Factor standardization played a central role in the multivariate analysis, where the Variance Inflation Factor (VIF) method in SPSS was applied to resolve multicollinearity issues. Predictors with VIF values exceeding 10 were substituted with alternatives falling below this limit. Land use and land cover data were obtained from Landsat images for the years 1991, 2001, 2011, and 2021, each at a 30-meter resolution. Results indicate that the proportion of built-up areas increased from 8% in 1991 to 11% in 2021, while vegetation cover decreased from 43% to 45%. During the same period, barren land reduced from 47% to 40%, and water bodies expanded from 3% to 4%. Future projections of built-up areas identify the most suitable zones for urban growth. The LR model integrates multiple variables—such as railways, primary roads, tracks, commercial zones, educational and health facilities, and economic hubs—using tools including SPSS, IDRISI, and ArcMap. IDRISI Selva is applied for future land use modeling, estimating that built-up areas will cover 161.22 km² by 2050. The prediction results indicate that population growth will continue to be a significant driver of built-up expansion in Hangu District
A GIS-Based Comparative Analysis of Ground Water Quality in Administrative Towns of Lahore City (2014–2024)
Full text linkAmidst insufficiency of water reserves, groundwater plays a crucial role in meeting freshwater requirements for households, parks, and horticulture in Lahore city. However, concerns regarding groundwater quality and its associated risk to human health and the environment have intensified due to factors such as rapid urbanization, industrial growth, and over extraction. Despite different monitoring efforts, the regional variability and uncertainty in groundwater quality necessitate more sophisticated assessment approaches to support optimal decision-making. The purpose of this study is to perform a comparative analysis between traditional Groundwater Quality Index (GQI) evaluation methods and entropy-weighted models. Additionally, it aims to analyze groundwater quality in tubewells across administrative towns in Lahore by utilizing Geographic Information System (GIS) and advanced geospatial techniques. The study incorporates groundwater quality data such as pH, Total Dissolved Solids (TDS), Electrical Conductivity (EC), and heavy metal concentrations into a GIS-based spatial analytic framework. By taking uncertainty in water quality classification into account, both traditional and entropy information theory offer a more adaptable and practical evaluation of groundwater suitability than the other GQI frameworks. The findings of this study show that groundwater quality varies significantly around Lahore, with certain regions showing contamination levels above acceptable bounds. High-risk areas are identified by the study, where water quality metrics point to possible health issues and highlight the necessity of focused measures
Spatio-Temporal Analysis of Meteorological Drought in Lahore (1995–2024)
Full text linkEvaluating urban meteorology is essential for efficient water resource management, especially considering climate change and an increase in Urban population. It helps to understand the severity and scope of drought conditions, which enables improved planning and execution of drought response measures. This research paper examines long-term patterns of rainfall variability and drought situations in Lahore, spanning a period of 30 years (1995- 2024). Monthly rainfall data taken from the UCSB-CHG/CHIRPS dataset, along with potential evapotranspiration (PET) information from the TERRACLIMATE dataset, are being analyzed using the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). The whole dataset is processed by Google Earth Engine (GEE), with Lahore’s administrative boundaries used to define the Area of Interest (AOI). The analysis recognizes significant annual and spatial variability, with the mean annual Precipitation recorded at 65.35mm. Extreme years included 2021, with 184.77 mm, and 2019, in this year precipitation was only recorded at 13.06 mm, which highlights growing climatic inconsistencies. SPI values dipped as low as -2.6 in 2015 in the southern part of Lahore, indicating severe drought conditions, while northern Lahore experienced values as high as 1.7, denoting extreme wetness. SPEI values exhibited a similar pattern, with the southern region recorded -2.3 in 2024, reflecting ongoing moisture stress, contrasted by northern Lahore reaching 1.2 to 2, a marked improvement in hydrological balance. These results show that Lahore is becoming more and more vulnerable to both drought and flooding because of urban growth and changes in the monsoon. According to the findings, localized, data-driven climate adaptation policies that prioritize drought resistance, water conservation, and efficient urban planning are essential
Trend Analysis and Prediction for Extreme Temperature of Lahore, Pakistan
Full text linkThis paper aims to examine the trends of the maximum temperature (Tmax) and minimum temperature (Tmin) in Lahore over a period of 42 years (1988 to 2029). The study employs the Mann-Kendall statistical test to analyze the linear trends of both Tmin and Tmax annually and seasonally. To determine the linear trends in temperature extremes (Te), a linear curve fitting method was employed. In modeling Tmax and Tmin, a sine function was utilized. The results showed that Tmin exhibited an increasing trend both annually and seasonally, except for winter, where no significant trend was observed. Conversely, Tmax showed a decreasing trend both annually and seasonally, except for the monsoon and pre-monsoon periods, where no significant trends were found. Furthermore, the study divided the Te data from 1988 to 2019 into two time series: from 1988 to 2003 and from 2004 to 2019. The findings indicated that Tmin had no significant trend, while Tmax demonstrated an increasing trend for the first time series. In contrast, both Tmin and Tmax exhibited increasing trends for the second time series. Moreover, when the time series was divided into six parts for trend analysis, mixed trends, whether increasing or decreasing, were observed. To investigate the periodicity of Te, the sine function was applied, and the results showed that Tmin had no periodicity. However, Tmax exhibited periodicity, and it was observed that the peak pattern repeated in reverse after 2004. Based on the proposed sine function model, the study predicted the future pattern of the maximum temperature variation in Lahore for the next ten years (i.e., 2020 to 2029)
Quantifying Crop Residue Burning in Punjab, Pakistan: A GEE-Based Assessment of Air Pollution
Full text linkCrop residue burning has become a common agricultural practice in developing countries due to numerous economic and social factors. To delineate the current pollution generated from crop residue burning in Punjab, Pakistan, a detailed study was conducted based on district-level crop production data from 2020 to 2024. The extraction of agricultural land was gathered from the European Space Agency/Climate Change Initiative (ESA/CCI), and the active fire count data were acquired from the VIIRS 375 m FIRMS standard active fire product. Air quality parameters, including CO and CH4, were assessed using Sentinel 5SP Tropomi. It was determined from the results that approximately 27% of the burned area increased from the years 2020-2021, accounting for a rapid increase from 31,984.15 sq km to 40,651.86 sq km, correlated with high CO and CH4 concentrations in 2021 and 2024, respectively, whereas a 17% decline occurred from the years 2022 to 2023, accounting for a decrease from 37,008.54 sq km to 33,710.85 sq km. However, it increased again by approximately 5.9% from 2023 to 2024 (33,710.85 sq km to 35,691.10 sq km). Using GEE, our study demonstrates the application of satellite data to map agricultural residue burning, and this information can provide valuable insights for policy formulation and managing crop residue practices
An Intelligent Intrusion Detection System Using Ensemble Learning for Ultra-Dense IoT Networks
Full text linkIntrusion detection refers to the process of observing and analyzing network or system incidents in a perpetual manner to identify unauthorized accesses, malicious acts, or violations of the rules. It plays a pivotal role in the protection of critical information, the prevention of security breaches, and the safety, confidentiality, and availability of company assets. Strong methods to identify and stop harmful activity are required because cybersecurity threats have grown more complex due to the quick expansion of digital infrastructure. Various researchers have conducted different research studies for intrusion detection, and different methodologies, along with traditional as well as machine learning models, have been applied with various datasets for the proposed task. This research aims to address these challenges by developing an efficient and intelligent intrusion detection system using a stacking ensemble learning approach. The proposed model integrates multiple base classifiers: Decision Tree, Naïve Bayes, K-Nearest Neighbor (KNN), and Linear Discriminant Analysis (LDA) to capture diverse decision boundaries, with a Random Forest acting as the meta-classifier to aggregate and optimize final predictions. The publicly available UNSW-NB15 dataset is employed in this study for intrusion detection. Python and its libraries are used for simulation purposes. After simulation, it has been achieved that the stacked model, which combines the predictions of multiple base learners through a meta-classifier, achieved a significantly higher accuracy of 99.93%. While in comparison, LDA achieved the highest accuracy of 94.25%, followed closely by SVM at 93.05%, DT at 91.00%, NB at 90.55%, and KNC at 89.81%. This demonstrates that ensemble learning, particularly stacking, can effectively leverage the strengths of individual models to greatly enhance intrusion detection performance for complex datasets
Automated Detection and Classification of Tomato Leaf Diseases Using EfficientNetB0 and Deep Learning Techniques
Full text linkTomato leaf diseases significantly impact agricultural productivity worldwide, necessitating accurate and timely detection methods. This research proposes a robust and efficient deep learning framework leveraging the “EfficientNetB0” architecture for the detection and classification of multiple tomato leaf diseases. Utilizing transfer learning alongside advanced data augmentation techniques, the model was trained on a comprehensive dataset comprising six disease categories and healthy samples, sourced from Kaggle. The proposed approach achieved an overall accuracy of 88.4%, outperforming traditional methods such as CNN, AlexNet, and S-V-M by a notable margin across all disease classes. Evaluation metrics, including precision, recall, and F1-score, further validate the model’s ability to accurately distinguish subtle disease symptoms despite class imbalance challenges. Additionally, the lightweight design of “EfficientNetB0” enables potential real-time applications in mobile and edge computing environments. These findings highlight the model’s promise as an effective tool for precision agriculture, facilitating early disease intervention and reducing crop loss. Future work will focus on expanding the dataset diversity and deploying the system in real-world agricultural settings through mobile and drone platforms
IoT-Enabled Smart Agriculture: Architectures, Applications, and Future Directions
Full text linkIntroduction/Importance of Study: The integration of Internet of Things (IoT) technologies into agriculture has become essential to tackle challenges of food security, climate change, and resource optimization.
Novelty statement: This study introduces the novelty of a unified, low-cost, and modular IoT framework that addresses gaps in scalability, interoperability, and affordability, particularly in developing agricultural regions.
Material and Method: A systematic literature review was conducted across IEEE Xplore, SpringerLink, ScienceDirect, MDPI, and Google Scholar, focusing on twelve peer-reviewed studies published between 2019 and 2024. Comparative thematic analysis was applied to examine IoT architectures, communication protocols, and practical implementations.
Result and Discussion: Findings highlight that IoT systems commonly adopt a three-layer architecture (perception, network, and application), with LoRa, Zigbee, and fog computing models offering reliable rural connectivity. Reported outcomes include 30–40% water savings through smart irrigation, 15–20% yield increases with IoT-based monitoring, and up to 16% energy efficiency improvements in optimized wireless sensor networks. Despite these advances, challenges remain in cost, interoperability, farmer training, and security mechanisms. Current frameworks also lack adaptability across diverse farming contexts, limiting scalability and long-term sustainability.
Concluding Remarks: IoT-enabled agriculture offers significant potential to enhance sustainability and productivity, but future research must prioritize modular platforms, lightweight AI integration, energy harvesting, and context-specific deployment strategies